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Application of machine learning to thermal management of solid-state hydrogen storage: A comprehensive review

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  • Shen, Shuo
  • Xu, Zhuanghua
  • Dong, Fei
  • Xu, Sheng
  • Yin, Bifeng

Abstract

Thermal management of metal hydride (MH) hydrogen storage systems is critically important to maintain the hydrogen absorption and release rates at desired levels. Implementing thermal management arrangements introduces challenges at system level mostly related to system's overall mass, volume, energy efficiency, complexity and maintenance, long-term durability, and cost. Low effective thermal conductivity (ETC) of the MH bed (∼0.1e0.3 W/mK) is a well-known challenge for effective implementation of different thermal management techniques. This paper comprehensively reviews thermal management solutions for the MH hydrogen storage used in fuel cell systems by also focusing on heat transfer enhancement techniques and assessment of heat sources used for this purpose. The literature recommended that the ETC of the MH bed should be greater than 2 W/mK, and heat transfer coefficient with heating/cooling media should be in the range of 1000e1200 W/m2K to achieve desired MH's performance. Furthermore, alternative heat sources such as fuel cell heat recovery or capturing MH heat during charging and releasing it back during discharging have also been thoroughly reviewed here. Finally, this review paper highlights the gaps and suggests directions accordingly for future research on thermal management for MH systems.

Suggested Citation

  • Shen, Shuo & Xu, Zhuanghua & Dong, Fei & Xu, Sheng & Yin, Bifeng, 2025. "Application of machine learning to thermal management of solid-state hydrogen storage: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:rensus:v:223:y:2025:i:c:s1364032125006835
    DOI: 10.1016/j.rser.2025.116010
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